Issue 24, 2023

First-principles microkinetic modeling of partial methane oxidation over graphene-stabilized single-atom Fe-catalysts

Abstract

Catalytic conversion of CH4 to transportable liquid hydrocarbons via partial oxidation is a promising avenue towards efficient utilization of natural gas. Single Fe atoms on N-functionalized graphene (FeN4/GN) have recently been shown to be active for partial CH4 oxidation with H2O2 at room temperature. Here, density functional theory (DFT) calculations combined with mean-field microkinetic modeling (MKM) have been applied to obtain kinetic understanding of partial CH4 oxidation with H2O2 to CH3OH and CH3OOH over FeN4/GN. CH3OH and CH3OOH are found to be minor and major reaction products, respectively, with a selectivity in agreement with reported experimental data. The kinetic modeling reveals two pathways for CH3OH production together with a dominant catalytic cycle for CH3OOH formation. The selectivity is found to be sensitive to the temperature and H2O2 concentration, with the CH3OH selectivity increasing with increasing temperature and decreasing H2O2 concentration. Turnover frequencies of both CH3OH and CH3OOH are found to decrease over time, due to a change in the Fe formal oxidation state from +6 to +4; Fe(+6) is more active, but less stable than Fe(+4). The present work unravels the detailed reaction mechanism for partial oxidation of methane by FeN4/GN, rationalizes experimental observations and provides guidance for efficient room-temperature methane conversion by single-atom Fe-catalysts.

Graphical abstract: First-principles microkinetic modeling of partial methane oxidation over graphene-stabilized single-atom Fe-catalysts

Supplementary files

Article information

Article type
Paper
Submitted
25 Sep 2023
Accepted
09 Nov 2023
First published
10 Nov 2023

Catal. Sci. Technol., 2023,13, 6999-7010

Author version available

First-principles microkinetic modeling of partial methane oxidation over graphene-stabilized single-atom Fe-catalysts

S. Hong, M. M. Kauppinen, E. V. Miu, G. Mpourmpakis and H. Grönbeck, Catal. Sci. Technol., 2023, 13, 6999 DOI: 10.1039/D3CY01335A

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